Large volumes of data are often processed for purposes of identifying and acting on events that are “outliers.” As examples, a financial institution may monitor credit card transaction data for purposes of identifying outliers to detect fraudulent transactions; and image scan data (magnetic resonance imaging (MRI) scan data, for example) may be processed to identify outliers to detect tissue abnormalities.
The processed data may be a real time or near real time stream of data, which has multidimensional points. For example, a stream of credit card transaction data has points (the credit card transactions), which each have various dimensions, such as time of the transaction, the amount of purchase, goods or services classifier, the merchant city, the merchant country, etc. Whether a particular point is an outlier depends at least in part on the dimensions that are considered.